Computational Approaches to Alleviate Alarm Fatigue in Intensive Care Medicine: A Systematic Literature Review. Chromik, Jonas; Klopfenstein, Sophie Anne Ines; Pfitzner, Bjarne; Sinno, Zeena-Carola; Arnrich, Bert; Balzer, Felix; Poncette, Akira-Sebastian in Frontiers in Digital Health (2022). 4
Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.
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AbstractPatient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.
Forecasting Thresholds Alarms in Medical Patient Monitors using Time Series Models. Chromik., Jonas; Pfitzner., Bjarne; Ihde., Nina; Michaelis., Marius; Schmidt., Denise; Klopfenstein., Sophie; Poncette., Akira-Sebastian; Balzer., Felix; Arnrich., Bert (2022). 26–34.
Too many alarms are a persistent problem in today’s intensive care medicine leading to alarm desensitisation and alarm fatigue. This puts patients and staff at risk. We propose a forecasting strategy for threshold alarms in patient monitors in order to replace alarms that are actionable right now with scheduled tasks in an attempt to remove the urgency from the situation. Therefore, we employ both statistical and machine learning mod- els for time series forecasting and apply these models to vital parameter data such as blood pressure, heart rate, and oxygen saturation. The results are promising, although impaired by low and non-constant sampling frequencies of the time series data in use. The combination of a GRU model with medium-resampled data shows the best performance for most types of alarms. However, higher time resolution and constant sampling frequencies are needed in order to meaningfully evaluate our approach.
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AbstractToo many alarms are a persistent problem in today’s intensive care medicine leading to alarm desensitisation and alarm fatigue. This puts patients and staff at risk. We propose a forecasting strategy for threshold alarms in patient monitors in order to replace alarms that are actionable right now with scheduled tasks in an attempt to remove the urgency from the situation. Therefore, we employ both statistical and machine learning mod- els for time series forecasting and apply these models to vital parameter data such as blood pressure, heart rate, and oxygen saturation. The results are promising, although impaired by low and non-constant sampling frequencies of the time series data in use. The combination of a GRU model with medium-resampled data shows the best performance for most types of alarms. However, higher time resolution and constant sampling frequencies are needed in order to meaningfully evaluate our approach.
Extracting Alarm Events from the MIMIC-III Clinical Database. Chromik., Jonas; Pfitzner., Bjarne; Ihde., Nina; Michaelis., Marius; Schmidt., Denise; Klopfenstein., Sophie; Poncette., Akira-Sebastian; Balzer., Felix; Arnrich., Bert (2022). 328–335.
Lack of readily available data on ICU alarm events constitutes a major obstacle to alarm fatigue research. There are ICU databases available that aim to give a holistic picture of everything happening at the respective ICU. However, these databases do not contain data on alarm events. We utilise the vital parameters and alarm thresholds recorded in the MIMIC-III database in order to artificially extract alarm events from this database. Prior to that, we uncover, investigate, and mitigate inconsistencies we found in the data. The results of this work are an approach and an algorithm for cleaning the alarm data available in MIMIC-III and extract concrete alarm events from them. The data set generated by this algorithm is investigated in this work and can be used for further research into the problem of alarm fatigue.
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AbstractLack of readily available data on ICU alarm events constitutes a major obstacle to alarm fatigue research. There are ICU databases available that aim to give a holistic picture of everything happening at the respective ICU. However, these databases do not contain data on alarm events. We utilise the vital parameters and alarm thresholds recorded in the MIMIC-III database in order to artificially extract alarm events from this database. Prior to that, we uncover, investigate, and mitigate inconsistencies we found in the data. The results of this work are an approach and an algorithm for cleaning the alarm data available in MIMIC-III and extract concrete alarm events from them. The data set generated by this algorithm is investigated in this work and can be used for further research into the problem of alarm fatigue.
SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies. Chromik, Jonas; Kirsten, Kristina; Herdick, Arne; Kappattanavar, Arpita Mallikarjuna; Arnrich, Bert in Sensors (2022). 22(1)
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
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AbstractObservational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
